We are looking for a passionate, high-energy, and innovative AI Agent Developer Fresher to join our growing engineering team. In this role, you will work at the cutting edge of Agentic AI workflows, moving beyond simple chatbots to build intelligent, goal-oriented systems that plan, reason, and integrate with external APIs and tools. You will be mentored by senior engineers to design and deploy autonomous systems that solve practical business problems.
- Assist in Agent Development: Help design, build, and test LLM-powered AI agents capable of multi-step reasoning, planning, and task execution.
- Workflow & Tool Integration: Connect LLMs (OpenAI, Anthropic, Gemini, etc.) to third-party APIs, databases, and internal software platforms.
- Prompt Engineering & Testing: Write, evaluate, and optimize system prompts, few-shot examples, and chain-of-thought instructions for reliable agent behavior.
- RAG Implementation: Support the setup of Retrieval-Augmented Generation (RAG) pipelines and vector databases to give agents contextual memory and knowledge.
- Debugging & Evaluation: Monitor agent performance, troubleshoot unexpected failure loops or tool errors, and document edge cases.
- Research & Experimentation: Stay updated on emerging AI frameworks (like LangChain or LangGraph) and test new community innovations.
Who can apply?
0 to 1 years of professional experience in AI Agent Developer.
Strong skills in time-series data analysis, feature engineering, and model optimization.
Experience with cloud platforms (GCP, AWS, Azure), container technologies (Docker, Kubernetes), and microservices.
A proven record of deploying ML models and developing autonomous AI agents.
Knowledge of AI-driven process optimization tools including AI orchestration for enterprise workflows.
Experience in troubleshooting ML systems
Adept at proactive problem identification and resolution.
Certification in MLOps is a plus.
Skills you’ll bring
Hands-on experience with vector databases (Pinecone, Chroma, Milvus, Qdrant).Basic familiarity with containerization tools like Docker.Personal portfolio or GitHub repository showcasing at least 1–2 working AI agent or RAG projects.